How You Use Attribution Analysis for Stronger Marketing Insight

data warehouse as a solution

The number of touch points through which you interact with customers — and the ways they encounter your brand — has exploded in recent years. In the past, the choices were simple: you ran a print ad, a broadcast commercial, maybe direct mail, or some combination. Today there is search, online display, social media, mobile, blogs, aggregator sites, and the list goes on.

With the proliferation of customer touch points has also come increased scrutiny regarding effectiveness. What is the real value of a dollar spent in any given medium? What medium gives you the most bang for your buck? How can you maximize impact moving forward?

Again in the past, measurement was simple: you ran an ad, and assessed the difference in terms of awareness, traffic and sales. Today, ad exchanges offer insight into how many people clicked on your ad and came to your desired destination.

But what happens then?

Attribution analysis can provide the answer to that question. It can bring together data from a number of disparate sources both internal to your business and external in terms of customer outreach. It can help you determine which channels are most cost effective in generating a volume of responses. Most importantly, it can help you identify your best customers within that group and act on that information by tweaking your marketing strategy accordingly moving forward.

How can you utilize attribution analysis effectively and reap these benefits? Here's a quick case study on how one company did it:

The Use Case for Attribution Analysis

A mobile productivity company markets an application that lets users create, review and share documents from any device. Early on, the company implemented third-party analytics tools with prebuilt dashboards to track basic metrics like downloads, daily/monthly user counts, time spent with the app, number of documents created, etc.

One Size Analytics Does Not Fit All

As the company's growth exploded and their user count grew into the millions, this one-size-fits-all approach to insights didn't scale. Their third-party analytics service could not handle the integration of real-time data from multiple sources such as server platform logs, website traffic and ad campaigns.

What's more, the company needed to analyze attribution across multiple screens and channels to help them decide where the next incremental marketing dollar would best be spent for new customer acquisition. A typical scenario was this: a user saw the company's Facebook ad while on their phone, then searched for reviews about the company on their laptop, and finally clicked to install the app from a display ad on their tablet. Attribution in this case requires splitting the credit for acquiring that new customer across social media on mobile, paid search/reviews on the PC and in-app display ads on tablets.

The company needed to take things a step further and discover which online marketing source helped them acquire their most valuable users. They needed to identify user behaviors — beyond the generic click-to-install action — that were unique to the app and made the user valuable to the company. In its early days, Facebook developed a simple but powerful way to do this: they discovered that the number of people a user “friends” within a given number of days of sign-up was a great predictor of how engaged or valuable a user would be in the long run. Online media and third-party analytics systems are blind to these kinds of time-displaced, complex actions occurring within an app.

They needed custom attribution analysis to do the job.

Attribution Analysis is the Solution

Starting simply, the company internally developed an initial objective: to discover precisely how any given user tends to interact with their product within a single session. Once that was determined, they could further drill down into that data to create profile segments of customers based on their status as paying users and the amount spent each month. By merging these two areas of data, the company was able to determine a given customers' lifetime value — a metric that defined which types of customers held the most revenue potential. That information, in turn, allowed them to more specifically target other users — ones that held the same “lifetime value” profile — through very specific media choices, with highly specific offers.

The result? Smarter, more informed use of marketing dollars. Continued growth. And a custom attribution analysis system in place that could grow and adapt as the company moved forward.

A Successful Attribution Analysis

When you begin to engage in attribution analysis, it's important to first define success in your own terms — and keep it simple. Ask yourself, whom do I consider a good customer? Then ask, what are my objectives with that customer? You may choose to increase spend and solidify loyalty with your highest-value customers. Or, you may choose to determine where you can find more high-value customers just like them. It's really all up to you, and what's right for your organization.

In short, attribution analysis can be a very quick and easy way to bring together data from a number of internal and third-party sources, and make sense of that data in terms that you very specifically determine. You'll gain the insights you need to clearly define and meet your marketing objectives, then hone your strategy to achieve the highest ROI possible on every marketing dollar spent.

What is Data Warehouse as a Service

We recently wrote about how data technologies are on the rise for marketers. Data Warehouses provide a central repository that scales and provides great insight into your marketing efforts – enabling the ability to bring in massive volumes of customer, transaction, financial and marketing data. By capturing online, offline and mobile data in a central reporting database, marketers are able to analyze and obtain the answers they need when they need it. Building a data warehouse is quite an undertaking for the average company – but Data Warehouse as a Service (DWaaS) solves the issue for companies.

About BitYota Data Warehouse as a Service

This post was written with the assistance of BitYota. BitYota's Data Warehouse as a Service solution takes the headache out of having to set up and manage another data platform. BitYota enables marketers to quickly get their data warehouse up and running, easily connecting to a cloud provider and configuring your warehouse. The technology utilizes SQL over JSON technology to easily query your warehouse and comes with real-time data feeds for fast analytics.

Attrribution Analysis - BitYota

One of the main inhibitors for fast analytics is the need to transform the data before storing it in your analytics system. In a world where applications change constantly, data arriving from multiple sources, and in different formats, means that companies often find themselves either spending too much time on data transformation projects or face broken analytics systems. BitYota stores and analyzes the data in its native format thus eliminating the need for laborious, time-consuming data transformation processes. Doing away with data transformation provides our customers with fast analytics, maximum flexibility, and complete data fidelity. BitYota

As your needs change, you're able to add or remove nodes from your cluster or change machine configurations. As a fully managed solution, BitYota monitors, manages, provisions, and scales your data platform, so that you can focus on what’s important – analyzing your data.

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